Stochastic Resonance and Its Signal-Processing Applications
Prof. G.V. Anand, Indian Institute of Science, Bangalore

Seminar • 24 June 2005 • BM.2.131

More Info ...Abstract
The phenomenon of stochastic resonance (SR), exhibited by the class of multistable nonlinear systems can be described as follows: The output SNR of the system shows a non-monotonic variation as the input noise intensity is varied at a fixed input signal power. We consider a symmetric 3-level quantizer as a simmple example of an SR system. For a given quantizer threshold, the output SNR attains a peak at the optimal value of input noise variance which depends on the noise pdf. Conversely, for a fixed input noise, the output SNR may be maximized by an optimal choice of the quantizer threshold. The peak SNR gain may exceed unity if the noise pdf is 'sufficiently' non-Gaussian. This phenomenon of SNR enhancement may be exploited in many signal processing applications involving non-Gaussian noise. We consider two applications, viz. signal dtection and direction-of-arrival estimation. We show that the performance of the processors at low SNR can be enhanced significantly by the use of SR, with negligible increase in computational or hardware complexity.